{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "b9R-4ezU3NH0"
      },
      "source": [
        "## AutoGraph: examples of simple algorithms\n",
        "\n",
        "This notebook shows how you can use AutoGraph to compile simple algorithms and run them in TensorFlow.\n",
        "\n",
        "It requires the nightly build of TensorFlow, which is installed below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        },
        "colab_type": "code",
        "id": "TuWj26KWz1fZ"
      },
      "outputs": [],
      "source": [
        "!pip install -U -q tf-nightly"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3kudk1elq0Gh"
      },
      "source": [
        "### Fibonacci numbers\n",
        "\n",
        "https://en.wikipedia.org/wiki/Fibonacci_number"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "height": 197
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 7512,
          "status": "ok",
          "timestamp": 1532101577266,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 240
        },
        "id": "H7olFlMXqrHe",
        "outputId": "472dbfe0-9449-4f93-e908-1a0785188a92"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 :  1\n",
            "1 :  2\n",
            "2 :  3\n",
            "3 :  5\n",
            "4 :  8\n",
            "5 :  13\n",
            "6 :  21\n",
            "7 :  34\n",
            "8 :  55\n",
            "9 :  89\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.contrib import autograph as ag\n",
        "\n",
        "\n",
        "def fib(n):\n",
        "  f1 = 0\n",
        "  f2 = 1\n",
        "  for i in range(n):\n",
        "    tmp = f2\n",
        "    f2 = f2 + f1\n",
        "    f1 = tmp\n",
        "    print(i, ': ', f2)\n",
        "  return f2\n",
        "\n",
        "\n",
        "with tf.Graph().as_default():\n",
        "  final_fib = ag.to_graph(fib)(tf.constant(10))\n",
        "  with tf.Session() as sess:\n",
        "    sess.run(final_fib)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "p8zZyj-tq4K3"
      },
      "source": [
        "#### Generated code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "height": 541
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 103,
          "status": "ok",
          "timestamp": 1532101577412,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 240
        },
        "id": "UeWjK8rHq6Cj",
        "outputId": "73ece895-12fb-489a-e52c-032945d7ed7a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "from __future__ import print_function\n",
            "import tensorflow as tf\n",
            "\n",
            "def tf__fib(n):\n",
            "  try:\n",
            "    with tf.name_scope('fib'):\n",
            "      f1 = 0\n",
            "      f2 = 1\n",
            "\n",
            "      def extra_test(f1_1, f2_1):\n",
            "        with tf.name_scope('extra_test'):\n",
            "          return True\n",
            "\n",
            "      def loop_body(i, f1_1, f2_1):\n",
            "        with tf.name_scope('loop_body'):\n",
            "          tmp = f2_1\n",
            "          f2_1 = f2_1 + f1_1\n",
            "          f1_1 = tmp\n",
            "          with ag__.utils.control_dependency_on_returns(ag__.utils.\n",
            "              dynamic_print(i, ': ', f2_1)):\n",
            "            f2, i_1 = ag__.utils.alias_tensors(f2_1, i)\n",
            "            return f1_1, f2\n",
            "      f1, f2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range, n),\n",
            "          extra_test, loop_body, (f1, f2))\n",
            "      return f2\n",
            "  except:\n",
            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(ag.to_code(fib))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eIfVy6ZTrFEH"
      },
      "source": [
        "### Fizz Buzz\n",
        "\n",
        "https://en.wikipedia.org/wiki/Fizz_buzz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "height": 125
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 233,
          "status": "ok",
          "timestamp": 1532101577681,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 240
        },
        "id": "33CAheYsrEQ7",
        "outputId": "82a493ee-15b5-419d-8c9c-5f4159090a05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Buzz\n",
            "11\n",
            "Fizz\n",
            "13\n",
            "14\n",
            "FizzBuzz\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.contrib import autograph as ag\n",
        "\n",
        "def fizzbuzz(i, n):\n",
        "  while i \u003c n:\n",
        "    msg = ''\n",
        "    if i % 3 == 0:\n",
        "      msg += 'Fizz'\n",
        "    if i % 5 == 0:\n",
        "      msg += 'Buzz'\n",
        "    if msg == '':\n",
        "      msg = tf.as_string(i)\n",
        "    print(msg)\n",
        "    i += 1\n",
        "  return i\n",
        "\n",
        "with tf.Graph().as_default():\n",
        "  final_i = ag.to_graph(fizzbuzz)(tf.constant(10), tf.constant(16))\n",
        "  with tf.Session() as sess:\n",
        "    sess.run(final_i)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lkq3DBGOv3fA"
      },
      "source": [
        "#### Generated code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "height": 1081
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 289,
          "status": "ok",
          "timestamp": 1532101578003,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 240
        },
        "id": "bBhFIIaZrxvx",
        "outputId": "d076a7ea-e643-4689-f90a-57f5d086dedc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "from __future__ import print_function\n",
            "import tensorflow as tf\n",
            "\n",
            "def tf__fizzbuzz(i, n):\n",
            "  try:\n",
            "    with tf.name_scope('fizzbuzz'):\n",
            "\n",
            "      def loop_test(i_1):\n",
            "        with tf.name_scope('loop_test'):\n",
            "          return tf.less(i_1, n)\n",
            "\n",
            "      def loop_body(i_1):\n",
            "        with tf.name_scope('loop_body'):\n",
            "          msg = ''\n",
            "\n",
            "          def if_true():\n",
            "            with tf.name_scope('if_true'):\n",
            "              msg_1, = msg,\n",
            "              msg_1 += 'Fizz'\n",
            "              return msg_1,\n",
            "\n",
            "          def if_false():\n",
            "            with tf.name_scope('if_false'):\n",
            "              return msg,\n",
            "          msg = ag__.utils.run_cond(tf.equal(i_1 % 3, 0), if_true, if_false)\n",
            "\n",
            "          def if_true_1():\n",
            "            with tf.name_scope('if_true_1'):\n",
            "              msg_2, = msg,\n",
            "              msg_2 += 'Buzz'\n",
            "              return msg_2,\n",
            "\n",
            "          def if_false_1():\n",
            "            with tf.name_scope('if_false_1'):\n",
            "              return msg,\n",
            "          msg = ag__.utils.run_cond(tf.equal(i_1 % 5, 0), if_true_1, if_false_1\n",
            "              )\n",
            "\n",
            "          def if_true_2():\n",
            "            with tf.name_scope('if_true_2'):\n",
            "              msg_3, = msg,\n",
            "              msg_3 = tf.as_string(i_1)\n",
            "              return msg_3,\n",
            "\n",
            "          def if_false_2():\n",
            "            with tf.name_scope('if_false_2'):\n",
            "              return msg,\n",
            "          msg = ag__.utils.run_cond(tf.equal(msg, ''), if_true_2, if_false_2)\n",
            "          with ag__.utils.control_dependency_on_returns(ag__.utils.\n",
            "              dynamic_print(msg)):\n",
            "            msg_4 = ag__.utils.alias_tensors(msg)\n",
            "            i_1 += 1\n",
            "            return i_1,\n",
            "      i = ag__.while_stmt(loop_test, loop_body, (i,), (tf, n, ag__, i))\n",
            "      return i\n",
            "  except:\n",
            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(ag.to_code(fizzbuzz))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BNRtprSvwJgk"
      },
      "source": [
        "### Conway's Game of Life\n",
        "\n",
        "https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "r8_0ioEuAI-a"
      },
      "source": [
        "#### Testing boilerplate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        },
        "colab_type": "code",
        "id": "7moIlf8VABkl"
      },
      "outputs": [],
      "source": [
        "NUM_STEPS = 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QlEvfIQPAYF5"
      },
      "source": [
        "#### Game of Life for AutoGraph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        },
        "colab_type": "code",
        "id": "5pCK2qQSAAK4"
      },
      "outputs": [],
      "source": [
        "#@test {\"skip\": true} \n",
        "NUM_STEPS = 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "height": 308
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 14892,
          "status": "ok",
          "timestamp": 1532101593030,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 240
        },
        "id": "hC3qMqryPDHS",
        "outputId": "8405c0e9-e518-41d6-f5bc-e78df6474169"
      },
      "outputs": [
        {
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              "\"\u003e\n",
              "  Your browser does not support the video tag.\n",
              "\u003c/video\u003e"
            ],
            "text/plain": [
              "\u003cIPython.core.display.HTML at 0x7f84b2253b50\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import time\n",
        "import traceback\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib import animation as anim\n",
        "import tensorflow as tf\n",
        "from tensorflow.contrib import autograph as ag\n",
        "from IPython import display\n",
        "\n",
        "\n",
        "@ag.do_not_convert(ag.RunMode.PY_FUNC)\n",
        "def render(boards):\n",
        "  fig = plt.figure()\n",
        "\n",
        "  ims = []\n",
        "  for b in boards:\n",
        "    im = plt.imshow(b, interpolation='none')\n",
        "    im.axes.get_xaxis().set_visible(False)\n",
        "    im.axes.get_yaxis().set_visible(False)\n",
        "    ims.append([im])\n",
        "\n",
        "  try:\n",
        "    ani = anim.ArtistAnimation(\n",
        "        fig, ims, interval=100, blit=True, repeat_delay=5000)\n",
        "    plt.close()\n",
        "\n",
        "    display.display(display.HTML(ani.to_html5_video()))\n",
        "  except RuntimeError:\n",
        "    print('Coult not render animation:')\n",
        "    traceback.print_exc()\n",
        "\n",
        "\n",
        "def gol_episode(board):\n",
        "  directions = tf.constant(\n",
        "      ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)))\n",
        "\n",
        "  new_board = []\n",
        "  ag.set_element_type(new_board, tf.int32)\n",
        "\n",
        "  for i in range(len(board)):\n",
        "    for j in range(len(board[i])):\n",
        "      num_neighbors = 0\n",
        "      for d in directions:\n",
        "        ni = i + d[0]\n",
        "        nj = j + d[1]\n",
        "        if ni \u003e= 0 and nj \u003e= 0 and ni \u003c len(board) and nj \u003c len(board[i]):\n",
        "          num_neighbors += board[ni][nj]\n",
        "      \n",
        "      new_cell = 0\n",
        "      if num_neighbors == 2:\n",
        "        new_cell = board[i][j]\n",
        "      elif num_neighbors == 3:\n",
        "        new_cell = 1\n",
        "      \n",
        "      new_board.append(new_cell)\n",
        "  final_board = ag.stack(new_board)\n",
        "  final_board = tf.reshape(final_board, board.shape)\n",
        "  return final_board\n",
        "  \n",
        "\n",
        "def gol(initial_board):\n",
        "  board = initial_board\n",
        "  boards = []\n",
        "  ag.set_element_type(boards, tf.int32)\n",
        "  # We are being explicit about tensor constants to ensure the loop\n",
        "  # is not unrolled in the graph. This may change in the future.\n",
        "  for i in range(tf.constant(NUM_STEPS)):\n",
        "    board = gol_episode(board)\n",
        "    boards.append(board)\n",
        "  boards = ag.stack(boards)\n",
        "  render(boards)\n",
        "  return tf.no_op()\n",
        " \n",
        "\n",
        "with tf.Graph().as_default():\n",
        "  # Gosper glider gun\n",
        "  # Adapted from http://www.cplusplus.com/forum/lounge/75168/\n",
        "  _ = 0\n",
        "  initial_board = tf.constant((\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n",
        "      ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,1,_,1,1,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
        "  ))\n",
        "  initial_board = tf.pad(initial_board, ((0, 20), (0, 10)))\n",
        "  \n",
        "  tf_gol = ag.to_graph(gol)\n",
        "  game_ops = tf_gol(initial_board)\n",
        "  with tf.Session() as sess:\n",
        "    sess.run(game_ops)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7NgrSPCZxs3h"
      },
      "source": [
        "#### Generated code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "height": 2323
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 753,
          "status": "ok",
          "timestamp": 1532101593840,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 240
        },
        "id": "hIGYeX0Cxs3i",
        "outputId": "e0b62eb1-3e12-4e53-dc54-8a3fa56d823d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "from __future__ import print_function\n",
            "import tensorflow as tf\n",
            "\n",
            "def tf__gol_episode(board):\n",
            "  try:\n",
            "    with tf.name_scope('gol_episode'):\n",
            "      directions = tf.constant(((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1),\n",
            "          (1, -1), (1, 0), (1, 1)))\n",
            "      new_board = ag__.new_list([])\n",
            "\n",
            "      def extra_test_2(new_board_2):\n",
            "        with tf.name_scope('extra_test_2'):\n",
            "          return True\n",
            "\n",
            "      def loop_body_2(i, new_board_2):\n",
            "        with tf.name_scope('loop_body_2'):\n",
            "\n",
            "          def extra_test_1(new_board_1):\n",
            "            with tf.name_scope('extra_test_1'):\n",
            "              return True\n",
            "\n",
            "          def loop_body_1(j, new_board_1):\n",
            "            with tf.name_scope('loop_body_1'):\n",
            "              num_neighbors = 0\n",
            "\n",
            "              def extra_test(num_neighbors_2):\n",
            "                with tf.name_scope('extra_test'):\n",
            "                  return True\n",
            "\n",
            "              def loop_body(d, num_neighbors_2):\n",
            "                with tf.name_scope('loop_body'):\n",
            "                  ni = i + ag__.get_item(d, (0), opts=ag__.GetItemOpts(\n",
            "                      element_dtype=None))\n",
            "                  nj = j + ag__.get_item(d, (1), opts=ag__.GetItemOpts(\n",
            "                      element_dtype=None))\n",
            "\n",
            "                  def if_true():\n",
            "                    with tf.name_scope('if_true'):\n",
            "                      num_neighbors_1, = num_neighbors_2,\n",
            "                      num_neighbors_1 += ag__.get_item(ag__.get_item(board,\n",
            "                          (ni), opts=ag__.GetItemOpts(element_dtype=None)),\n",
            "                          (nj), opts=ag__.GetItemOpts(element_dtype=None))\n",
            "                      return num_neighbors_1,\n",
            "\n",
            "                  def if_false():\n",
            "                    with tf.name_scope('if_false'):\n",
            "                      return num_neighbors_2,\n",
            "                  num_neighbors_2 = ag__.utils.run_cond(tf.logical_and(tf.\n",
            "                      greater_equal(ni, 0), tf.logical_and(tf.greater_equal\n",
            "                      (nj, 0), tf.logical_and(tf.less(ni, ag__.utils.\n",
            "                      dynamic_builtin(len, board)), tf.less(nj, ag__.utils.\n",
            "                      dynamic_builtin(len, ag__.get_item(board, (i), opts=\n",
            "                      ag__.GetItemOpts(element_dtype=None))))))), if_true,\n",
            "                      if_false)\n",
            "                  return num_neighbors_2,\n",
            "              num_neighbors = ag__.for_stmt(directions, extra_test,\n",
            "                  loop_body, (num_neighbors,))\n",
            "              new_cell = 0\n",
            "\n",
            "              def if_true_2():\n",
            "                with tf.name_scope('if_true_2'):\n",
            "                  new_cell_2, = new_cell,\n",
            "                  new_cell_2 = ag__.get_item(ag__.get_item(board, (i), opts\n",
            "                      =ag__.GetItemOpts(element_dtype=None)), (j), opts=\n",
            "                      ag__.GetItemOpts(element_dtype=None))\n",
            "                  return new_cell_2,\n",
            "\n",
            "              def if_false_2():\n",
            "                with tf.name_scope('if_false_2'):\n",
            "                  new_cell_3, = new_cell,\n",
            "\n",
            "                  def if_true_1():\n",
            "                    with tf.name_scope('if_true_1'):\n",
            "                      new_cell_1, = new_cell_3,\n",
            "                      new_cell_1 = 1\n",
            "                      return new_cell_1,\n",
            "\n",
            "                  def if_false_1():\n",
            "                    with tf.name_scope('if_false_1'):\n",
            "                      return new_cell_3,\n",
            "                  new_cell_3 = ag__.utils.run_cond(tf.equal(num_neighbors, \n",
            "                      3), if_true_1, if_false_1)\n",
            "                  return new_cell_3,\n",
            "              new_cell = ag__.utils.run_cond(tf.equal(num_neighbors, 2),\n",
            "                  if_true_2, if_false_2)\n",
            "              new_board_1 = ag__.list_append(new_board_1, new_cell)\n",
            "              return new_board_1,\n",
            "          new_board_2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range,\n",
            "              ag__.utils.dynamic_builtin(len, ag__.get_item(board, (i),\n",
            "              opts=ag__.GetItemOpts(element_dtype=None)))), extra_test_1,\n",
            "              loop_body_1, (new_board_2,))\n",
            "          return new_board_2,\n",
            "      new_board = ag__.for_stmt(ag__.utils.dynamic_builtin(range, ag__.\n",
            "          utils.dynamic_builtin(len, board)), extra_test_2, loop_body_2, (\n",
            "          new_board,))\n",
            "      final_board = ag__.list_stack(new_board, opts=ag__.ListStackOpts(\n",
            "          element_dtype=tf.int32, original_call=ag.stack))\n",
            "      final_board = tf.reshape(final_board, board.shape)\n",
            "      return final_board\n",
            "  except:\n",
            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
            "\n",
            "def tf__gol(initial_board):\n",
            "  try:\n",
            "    with tf.name_scope('gol'):\n",
            "      board = initial_board\n",
            "      boards = ag__.new_list([])\n",
            "\n",
            "      def extra_test(board_1, boards_1):\n",
            "        with tf.name_scope('extra_test'):\n",
            "          return True\n",
            "\n",
            "      def loop_body(i, board_1, boards_1):\n",
            "        with tf.name_scope('loop_body'):\n",
            "          board_1 = tf__gol_episode(board_1)\n",
            "          boards_1 = ag__.list_append(boards_1, board_1)\n",
            "          return board_1, boards_1\n",
            "      board, boards = ag__.for_stmt(ag__.utils.dynamic_builtin(range, tf.\n",
            "          constant(NUM_STEPS)), extra_test, loop_body, (board, boards))\n",
            "      boards = ag__.list_stack(boards, opts=ag__.ListStackOpts(\n",
            "          element_dtype=tf.int32, original_call=ag.stack))\n",
            "      with ag__.utils.control_dependency_on_returns(render(boards)):\n",
            "        boards_2 = ag__.utils.alias_tensors(boards)\n",
            "        return tf.no_op()\n",
            "  except:\n",
            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(ag.to_code(gol))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "p8zZyj-tq4K3",
        "Lkq3DBGOv3fA",
        "r8_0ioEuAI-a",
        "7NgrSPCZxs3h"
      ],
      "default_view": {},
      "last_runtime": {
        "build_target": "",
        "kind": "local"
      },
      "name": "Simple algorithms using AutoGraph",
      "provenance": [
        {
          "file_id": "19q8KdVF8Cb_fDd13i-WDOG_6n_QGNW5-",
          "timestamp": 1528465909719
        }
      ],
      "version": "0.3.2",
      "views": {}
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
